The 10 key points for time series analysis using Pandas (depending on the 10 due to space limitations, but more details may be involved in actual operation) are as follows:
1. Create time series data
Time series data refers to a numerical sequence formed at multiple time points. In Pandas, you can use the to_datetime function to convert a date string to a timestamp and create a DataFrame or Series object indexed with a timestamp.
import pandas as pd
# Create a simple DataFramedata = {'Date': ['2022-01-01', '2022-01-02', '2022-01-03'],
'Price': [100, 105, 110]}
df = (data)
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)
print(df)
2. Set date to index
To facilitate processing time series data, the date is usually set to the index of the DataFrame.
# Convert 'Date' column to datetime type and set as indexdf['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)
3. Data cleaning
Time series data is often accompanied by problems such as missing values, outliers, and non-standard time formats. Using Pandas' tools, data cleaning tasks can be completed efficiently.
Identify and fill missing values: Use the isnull() function to identify missing values, and use fillna() or interpolate() methods to fill in missing values.
Handling outliers: Use statistical methods (such as IQR interquartile range) to identify and process outliers.
4. Data resampling
Data resampling refers to re-adjusting time series data to different time frequencies, such as converting daily data into monthly data or annual data.
# Resample by month and calculate the averagemonthly_df = ('M').mean()
print(monthly_df)
5. Interpolation processing
When there are missing values in the time series data, the interpolation method can be used to fill these missing values. Pandas provides a variety of interpolation methods, such as linear interpolation, time interpolation, etc.
# Use linear interpolation to fill missing valuesdf['Price'] = df['Price'].interpolate()
6. Scrolling window analysis
Scrolling window analysis is a common technique in time series analysis. It allows the calculation of statistical indicators such as moving averages, moving standard deviations, etc. within a fixed-size window.
# Calculate the 5-day moving averagedf['MA_5'] = df['Price'].rolling(window=5).mean()
7. Seasonal Decomposition
Seasonal decomposition can help identify trends, seasonal, and random components in the data. Pandas can be used in combination with the statsmodels library for seasonal decomposition.
from import seasonal_decompose # Perform seasonal decompositionresult = seasonal_decompose(df['Price'], model='additive') print() print() print()
8. Lag and difference
Lag is to move time series data backward by a certain step, which is very useful when building time series models. The difference is to calculate the amount of change in time series data at different time points.
# Calculate the column with lag of 1df['Lag_1'] = df['Price'].shift(1) # Calculate first-order differencedf['Diff_1'] = df['Price'].diff()
9. Time-frequency conversion
Use Pandas' resample() method to change the frequency of the time series, such as converting it into daily data, weekly data, etc. In addition, the asfreq() method can also be used to handle discontinuous timestamps.
# Convert data to daily frequency and fill in missing valuesdaily_data = ('D').ffill()
10. Visual analysis
Finally, using Pandas and matplotlib and other libraries to visually analyze the time series data, and more intuitively display information such as trends, periodicity and outliers in the data.
import as plt # Draw raw time series data() ()
The above is a detailed explanation of the 10 key points used for time series analysis using Pandas. For more information about Pandas time series analysis, please pay attention to my other related articles!